Roland Faller

Acrylate-nitrile adhesives are used in electronics, aerospace, and defense applications, but the chemical mechanisms responsible for age-induced degradation of assemblies that use these adhesives are difficult to characterize directly. This work identifies acrylonitrile (AN)-rich local environments associated with the proposed 5:12 butyl acrylate-co-acrylonitrile (BA-AN) composition as important structural and electronic contributors to degradation in acrylate-nitrile adhesives. To interpret aging in this cross-linked adhesive, we developed an integrated workflow combining ensemble quantum-based steered molecular dynamics (SMD), multivariate spectral deconvolution, and electronic structure analysis. This approach links local sequence arrangement to hydrolysis pathways, product speciation, and assembly property degradation, allowing complex attenuated total reflectance Fourier transform infrared (ATR-FTIR) aging data to be interpreted at the molecular level. The modeled AN-rich environment exhibits approximately orthogonal neighboring nitrile dipoles that suppress interdipole coupling and favor cyclic amidine formation over terminal hydrolysis products. Spectral deconvolution of aged ATR-FTIR data estimates a total hydrolysis level of 45.3%, with the degraded matrix dominated by nitrogen-containing intermediates. Composition-guided electronic estimates give the pristine 5:12 HOMO–LUMO gap (Eg) nearly 1.0 eV below that of the 1:1 alternating model. In the computed aged-state, Eg decreases slightly from 6.77 to 6.66 eV, while the intensity-derived transition dipole moment (TDM) increases by 16.9%, from 3.34 to 3.90. This electronic shift is consistent with an enhanced dipolar dielectric loss and energy dissipation in the model aged state. The corresponding electronic quality factor, χ = Eg/TDM, is in good agreement with the experimentally observed 15.5% reduction in dielectric breakdown strength. Together, these results connect AN-rich local environments to hierarchical adhesive degradation and provide a broadly applicable framework for analyzing aging chemistry in complex polymer systems.

A critical step in the emergence of a virus is the ability of the viral protein to bind a host receptor and mediate cell entry. For many coronaviruses, this interaction occurs between the Spike S1 subunit and the human ACE2 receptor. Whether this binding interface can be computationally distinguished across unstudied viruses without experimentally resolved protein structures remains an open question. We predicted how 28 emerging coronaviruses may bind to human ACE2 using structural predictions, static interaction prediction programs, and molecular dynamics simulations. To screen the emerging coronaviruses, we predicted a library of S1 structures using AlphaFold. These predicted structures were then used to model the S1-ACE2 interaction with AlphaFold, ClusPro, and HADDOCK. We used known ACE2-binding sarbecoviruses as positive controls and coronaviruses that bind other receptors as negative controls to threshold predicted binding. Contact analysis quantified the predicted binding and revealed that these static interaction prediction methods varied in discriminative power. Less restrained static predictions separated binders from non-binders, whereas heavily restrained docking did not, potentially forcing an interaction where none should exist. This analysis highlighted an emerging coronavirus, Zhejiang2013, as a potential ACE2 binder. We used molecular dynamics simulations to further assess the static predictions and model the interaction over time. Overall, our results indicate that Zhejiang2013 exhibits dynamic interaction patterns consistent with ACE2 binding. Given that two ACE2-binding coronaviruses have caused global pandemics within the past two decades, identifying potential ACE2 binders is critical for early warning and pandemic preparedness.

HIV infection disrupts gut epithelial barrier integrity and mucosal immunity, driving chronic inflammation and disease progression which are not fully resolved despite anti-retroviral therapy. Here we identify the microbiota-derived octadecanoid-hydroxy-fatty-acid metabolite 10-hydroxystearic acid (10-HSA), produced by Lactiplantibacillus plantarum , as a key mediator of gut epithelial barrier repair in human intestinal epithelial cells in vitro, ex vivo and in the non-human primate model of HIV/AIDS. X-ray crystallography and transcriptomics combined with functional analyses revealed that 10-HSA directly binds PPARα, inducing lipid metabolism, mitochondrial regeneration and subsequent epigenetic histone crotonylation, thereby promoting gut epithelial renewal. Co-administration of 10-HSA with anti-retroviral therapy in SIV-infected macaques accelerated viral suppression, resolved systemic inflammation, repaired gut epithelial integrity and recovered the gut microbiota. These findings identify a microbiota-derived lipid metabolite–PPARα–histone crotonylation axis that activates gut epithelial regeneration. This study defines a host–microbiome metabolic pathway that restores epithelial–immune homeostasis and enhances the efficacy of anti-retroviral therapy.

Though "enhanced sampling methods," a class of computational tools, can help accelerate the exploration of the system's configuration space and dynamics in molecular dynamics (MD) simulations, obtaining accurate thermodynamic and kinetic properties of large systems within a computationally tractable period remains challenging. To tackle this issue, we developed a novel hybrid enhanced sampling method, parallelizable Gaussian accelerated molecular dynamics (ParGaMD), that runs many short Gaussian accelerated molecular dynamics (GaMD) simulations across multiple GPUs in parallel using the weighted ensemble method (WE) framework. Although GaMD accelerates sampling by adding a harmonic boost potential to the system, GaMD often takes weeks to run for large systems and does not parallelize well across multiple GPUs in the implemented MD simulation engines. By leveraging the WE framework's efficient GPU parallelization, we can overcome this bottleneck and additionally sample along the chosen collective variables, thereby making ParGaMD more powerful than GaMD itself. We show that ParGaMD can significantly accelerate sampling of different configuration states and dynamics across various systems, benefiting the broader scientific community.

Knowledge of AFM tip and lipid bilayer stack interactions is essential for designing and developing nano- to mesoscale lipid constructs. In this study, a model was developed to reveal the complex molecular mechanism governing tip–lipid interactions. This complexity in the interaction forces during penetration leading to a disturbance of lipid bilayers is explored using coarse-grained MARTINI molecular dynamics simulations for a multibilayer 1-palmitoyl-2-oleoylphosphatidylcholine (POPC) lipid system and an AFM tip apex. Force–distance profiles acquired during AFM tip penetration displayed a sawtooth pattern with a periodicity consistent with the bilayer thickness. This computation provides mechanistic insights into this process: a layer-by-layer penetration with distinct features in force–distance traces to specific molecular events such as climbing, adhesion, and compression. The outcomes benefit development of engineered lipid-based materials used in drug and vaccine delivery and fabrication of lipid-based nanodevices and biosensors.

Arachis hypogaea 2 (Ara h2), the major peanut allergen responsible for severe allergic reactions in a large proportion of the population, is a key target for the detection of peanut allergenicity. In this study, the ability of gold nanoparticles functionalized with a peptide group to bind Ara h2 was investigated using molecular dynamics (MD) simulations. According to the structure of the peanut allergen Ara h2, which binds to a high-affinity IgE receptor, 130 amino acids of IgE have considerable interaction with the Ara h2 allergen. Therefore, a new peptide comprising these amino acids was designed as a functionalization agent for gold nanoparticles. To investigate the role of nanoparticle shape in the dynamics and interactions between Ara h2 and the designed systems, 2 shapes of gold nanoparticles were selected: rod-shaped (NR-Pep) and spherical (NP-Pep). Moreover, given the widespread use of phosphate-buffered saline (PBS) in experimental studies to mimic physiological conditions, its influence on the stability and behavior of the complexes was investigated alongside commonly used standard physiological conditions in MD simulations. MD simulations show that buffer conditions enhance the structural stability and compactness of the Ara h2@IgE complex. Based on these results, Ara h2 was substantially affected when interacting with functionalized gold nanoparticles (NP-Pep and NR-Pep). Among the studied nanoparticles, the rod (NR-Pep) forms a more stable complex with Ara h2 than do NP-Pep and IgE, which is the human receptor for the peanut allergen. Multiple analyses confirm that the designed nanoparticles are promising candidates for adsorbing the peanut allergen Ara h2.

We present an AI-assisted pipeline for disease-specific drug landscape analysis. Given a disease name, the system assembles a comprehensive, evidence-based view of therapeutic assets by integrating structured sources (such as ClinicalTrials.gov and ChEMBL) and unstructured sources (such as publications, press releases, and patents). Large language models are used in a constrained, auditable mode to normalize drug aliases, resolve drug target/mechanism of action annotations, and harmonize program status across records. The output is a disease-centric map that spans preclinical assets, not-yet-approved assets (both active and discontinued/shelved), and FDA-approved drugs suitable for re-purposing. Assets are ranked using interpretable, evidence-based scoring heuristics that combine trial volume and clinical phase, endpoint outcomes, biomarker support, recency of activity, and regulatory designations, along with penalties for safety signals and non-pharmaceutical interventions, as well as proportional adjustments for operational versus scientific discontinuations. Case studies in Alzheimers disease, pancreatic cancer, and cystic fibrosis demonstrate generality, coverage, and discrimination across mechanisms and stages. This framework provides a transparent method to assemble and prioritize the therapeutic landscape for any disease, unifying disparate data into a coherent and analyzable representation.

Though ``enhanced sampling methods,'' a class of computational tools, can help accelerate the exploration of the configuration space and dynamics of the system of interest in molecular dynamics (MD) simulations, obtaining accurate thermodynamic and kinetic properties of large systems (>200,000 atoms) from MD simulations in a computationally tractable period is still challenging. To tackle this issue, we develop a novel enhanced sampling method called parallelized Gaussian accelerated molecular dynamics (ParGaMD) that runs many short Gaussian accelerated molecular dynamics (GaMD) simulations over multiple GPUs in parallel by using the weighted ensemble method (WE) framework. Although GaMD accelerates sampling by adding a harmonic boost potential to the system, GaMD often takes weeks to run for large systems and does not parallelize well over multiple GPUs in specific MD simulation engines. By using the efficient GPU parallelization of the WE framework, we can overcome this bottleneck and additionally sample along the chosen collective variables, which enables ParGaMD to be more powerful than GaMD itself. We show that ParGaMD can significantly speed up sampling different configuration states and dynamics of various systems, which can benefit the broader scientific community.